With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

To explore the value of general information and rapid laboratory tests obtained from the emergency department in the early diagnosis and prevention of young patients with acute myocardial infarction and acute myocarditis, in order to prevent the disease from progressing to a critical stage. This study employs a retrospective observational study, compiling clinical data from young patients diagnosed with acute myocardial infarction or acute myocarditis who were admitted to the Department of Cardiology or Emergency Department of the Second Affiliated Hospital of Soochow University from January 2015 to September 2024. Demographic information and laboratory test results from both the outpatient and emergency departments were retrieved. The acute myocardial infarction group comprised 267 patients (257 males, 10 females) aged 23-44 ys, while the acute myocarditis group included 134 patients (93 males, 41 females) aged 18-44 ys. A comparative analysis of the clinical data between the two groups was conducted, encompassing variables such as age, gender, comorbidities, high-risk factors, emergency blood routine tests, high-sensitivity C-reactive protein levels, coagulation profiles, renal function tests, NT-proBNP levels, myocardial injury markers, electrocardiogram readings, blood pressure, and heart rate. The results showed that:Compared with the young myocarditis group, the myocardial infarction group was older (ys)[38(35, 42) vs 30(25, 37), U=7 893, P<0.001], more male [257(96.3%) vs 93(69.4%), χ2=57.95, P<0.001], more smoking [211(79.0%) vs 38(28.4%), χ2=97.32, P<0.001], drinking history [125(46.8%) vs 22(16.4%), χ2=35.51, P<0.001], family history of coronary heart disease [45(16.9%) vs 3(2.2%), χ2=18.09, P<0.001], hypertension [100(37.5%) vs 12(9.0%), χ2=36, P<0.001] and diabetes [42(15.7%) vs 4(3.0%), χ2=14.27, P<0.001]. Systolic blood pressure (mmHg)[126(114, 144) vs 119(101, 126), U=11 389.50, P<0.001], diastolic blood pressure (mmHg)[80(70, 93) vs 72(62, 81), U=12 220.50, P<0.001], total white blood cell count (10 9/L)[11.3(9.2, 14.1) vs 8.5(6.6, 11.2), U=10 825.50, P<0.001], hemoglobin (g/L)[157(147, 166) vs 143(129, 154), U=9 404.50, P<0.001], platelet count (10 9/L)[244(206, 297) vs 207(173, 253), U=11 680, P<0.001], uric acid (μmol/L)[380(315, 446) vs 347(265, 412), U=14 805.50, P=0.005], ST segment elevation [204(76.4%) vs 57(42.5%), χ2=73.03, P<0.001] and Q wave formation [76(28.5%) vs 17(12.7%), χ2=12.47, P<0.001] in ECG were higher than those in myocarditis group. The duration of onset (hs) [6(3, 25) vs 48(24, 73), U=27911, P<0.001], heart rate (beats/min)[82(74, 92) vs 92(78, 103), U=22 347, P<0.001], D-dimer (μg/ml)[0.23(0.17, 0.51) vs 0.61(0.30, 1.38), U=25 806, P<0.001], High-sensitivity troponin T/99th percentile upper reference limit [5(1, 36) vs 16(8, 39), U=22 577, P<0.001], NT-proBNP (pg/ml) [204(64, 644) vs 824(189, 4 043), U=25 134, P<0.001], C-reactive protein (mg/L)[6(3, 9) vs 24(6, 55), U=26 349.50, P<0.001] and body temperature (℃) [36.50(36.30, 36.60) vs 37.35(36.50, 38.50), U=26 961, P<0.001] were significantly lower than those in myocarditis group, the symptoms of chest pain in myocardial infarction group was significantly higher than those in myocarditis group [262(98.1%) vs 83(61.9%), χ2=97.24, P<0.001], and the history of prodromal infection [12(4.5%) vs 112(83.6%), χ2=261.26, P<0.001], syncope [11(4.1%) vs 18(13.4%), χ2=11.53, P<0.001] and shock [6(2.2%) vs 22(16.4%), χ2=27.59, P<0.001] in myocardial infarction group were significantly lower than those in myocarditis group. With acute myocardial infarction as the target outcome, 8 influencing factors selected by LASSO regression, and 5 independent influencing factors were found after multiple Logistic regression, those were age ( OR=1.21, 95% CI: 1.12-1.31; P<0.001), pre-infection ( OR=0.02, 95% CI: 0.01-0.06; P<0.001), body temperature ( OR=0.37, 95% CI: 0.18-0.77; P=0.008), chest pain ( OR=26.75, 95% CI: 5.87-121.81; P<0.001) and white blood cell count ( OR=1.27, 95% CI: 1.12-1.44; P<0.001). Younger age, high body temperature and pre-infection are independent predictors for acute myocarditis, while chest pain and elevated white blood cell count are independent predictors for acute myocardial infarction. The five influencing factors selected by multivariate logistic regression and their combined diagnostic model were subjected to ROC analysis. The AUC reached 0.969, sensitivity reached 0.940 and specificity reached 0.925. Calibration curve and decision curve analysis(DCA) demonstrate that the model possesses excellent clinical application value. In conclusion, age, chest pain, pre-infection, body temperature and white blood cell count were independent factors in distinguishing acute myocardial infarction and acute myocarditis in young people. The clinical differential diagnosis model based on 5 independent factors may has high efficiency and good clinical practicability.

Objective:To analyze the associated factors and cumulative effects of cardiometabolic multimorbidity (CMM) among residents in southern Xinjiang.Methods:A stratified random cluster sampling method was used to conduct questionnaire surveys, physical examinations and laboratory tests among the personnel of the 51st Brigade, 3rd Division, Xinjiang, in 2016. The multivariate logistic regression, multivariate linear regression, restricted cubic spline, and network analysis methods were used to study the association of lifestyle (smoking, alcohol consumption and physical activity), socioeconomic (occupation, education and marital status) and clinical factors (waist circumference, body mass index and family history) with CMM.Results:A total of 12 773 study subjects were included. The prevalence of cardiovascular metabolic diseases among residents in southern Xinjiang was 52.49%. Specifically, the prevalence rates of dyslipidemia, hypertension, coronary heart disease, diabetes, and stroke were 31.14%, 29.95%, 6.78%, 6.26%, and 2.47%, respectively, and the prevalence of CMM was 19.06%. Multivariate logistic regression analysis revealed that the associations between clinical and socioeconomic factors and CMM significantly increased with higher scores. Specifically, the OR rose from 1.75 (clinical factors) and 1.07 (socioeconomic factors) on a score of 1 to 4.41 and 1.93 on a score of 3, respectively. The association between lifestyle factors and CMM was only observed at higher scores ( OR=1.26, 95% CI:1.07~1.62). The trend test using the scores of each group as continuous variables in the model showed that the risk of disease increased with the accumulation of clinical, socioeconomic and lifestyle factors (all P<0.05). Restricted cubic spline analysis demonstrated a non-linear relationship between the total number of associated factors and CMM ( Poverall<0.05 and Pnon-linear<0.05). Network analysis identified hypertension (strength=0.42) as the “core node” among the five diseases. When analyzing the three types of influencing factors, hypertension (strength=0.68), dyslipidemia (strength=0.47), coronary heart disease (strength=0.37), and clinical factors (strength=0.53) emerged as “core nodes”. In the network of nine associated factors, abnormal waist circumference and BMI (strength=0.90 and 0.84) were identified as “key factors”, while hypertension (strength=0.68) and dyslipidemia (strength=0.52) were identified as “key diseases”. Conclusion:The prevalence of CMM among residents in southern Xinjiang is high, and there is a cumulative effect of multiple factors. Hypertension and dyslipidemia are key diseases in the multimorbidity network, while abnormal BMI and waist circumference are key associated factors.

The epidemiological and spatiotemporal clustering characteristics of dengue fever in Fujian Province were ana-lyzed,to provide a scientific basis for dengue fever prevention and control.Descriptive epidemiology,spatial autocorrelation a-nalysis,and spatiotemporal scanning were used to analyze dengue fever cases in Fujian Province from 2011 to 2023.In this peri-od,a total of 3 586 cases of dengue fever were reported in Fujian Province,including 2 360 local cases,1 134 imported cases from abroad,and 92 imported cases from China.Cases were reported in ten prefectures and cities of the province,and 81 out of 88 counties reported cases.Imported cases were reported throughout the year in Fujian Province,but the occurrence of local ca-ses showed clear seasonality.Local cases and domestic imports were concentrated in August to October,whereas overseas im-ports occurred primarily from June to October.The imported cases were mainly from Southeast Asian countries,but a trend of spreading from Southeast Asian countries to South Asia,Africa,the Americas,and other regions,was observed.Spatio-tem-poral clustering of dengue fever was found in Fujian Province(Moran's I value 0.14-0.66,P＜0.05),and the high-high ag-gregation areas were distributed primarily in Fuzhou,Quanzhou,and Putian.Spatio-temporal scanning detected three aggrega-tion areas:one main and two secondary.The aggregation time was from the end of July to October,and the distribution was primarily in Fuzhou,Quanzhou,Putian,Zhangzhou,and Xiamen.The distribution of dengue fever in Fujian Province showed clear spatial and temporal clustering from the end of July to October,and the distribution was primarily in Fuzhou,Quanzhou,Putian,Zhangzhou,and Xiamen.For high concentration areas,national health campaigns,mosquito prevention and control,epidemic surveillance,medical personnel training,and other relevant measures could be carried out in advance before local cases appear every year.Reduce local transmission of dengue fever due to importation.

Aim To evaluate the role of the glucose transporter protein 1(GLUT1)-dependent glycolytic in the attenuation of oxygen-glucose deprivation-reoxygen-ation(OGD/R)injury in HK-2 cells by dexmedetomi-dine(Dex).Methods C57/BL6 mice were random-ly divided into three groups(n=6),namely,sham operation group(Sham group),renal ischemia reper-fusion group(I/R group)and Dex group(I/R+Dex group).Serum creatinine(Cr)and urea nitrogen(BUN)were measured,while the levels of key glyco-lytic enzymes HK2,PFKFB3 and GLUT1 were meas-ured.HK-2 cells were cultured and randomised into seven groups(n=6),which was treated with OGD/R,overexpression or interference with GLUT1,Dex and glycolysis inhibitor 2-DG.CCK-8 and LDH activi-ty were used to detect cellular damage.Glycolysis lev-els were detected by lactate and ECAR.The inflamma-tory level was reflected by qRT-PCR for IL-6 and TNF-α.qRT-PCR and Western blot were performed to de-tect the levels of GLUT1,HK2,and PFKFB3.Results Dex significantly ameliorated kidney injury and HK-2 cell injury(P＜0.05).Dex inhibited the OGD/R-induced rise in lactate and extracellular acidification rate(ECAR),as evidenced by suppression of the ex-pression of GLUT1,HK2 and PFKFB3(P＜0.05).In vitro experiments showed that GLUT1 knockdown sig-nificantly improved OGD/R-induced cellular damage.Lactate,ECAR,glycolysis-related mRNAs and pro-teins were inhibited by GLUT1 knockdown(P＜0.05).Significantly,there were no significant differ-ences in above indexes after Dex treatment based on GLUT1 knockdown.Overexpression of GLUT1 abroga-ted the protective effects of Dex,while reversing the inhibitory effects of Dex on the expression of GLUT1,HK2,and PFKFB3(P＜0.05).Conclusions Dexmedetomidine attenuates OGD/R induced injury in HK-2 cells by inhibiting GLUT1-dependent glycolysis.

At present,the laboratory medicine industry in China has developed rapidly,with various new technologies and methods emerge one after another. However,the current training process of laboratory medicine technology professionals still follows a training model that emphasizes theyory over practice,and the quality of training personnel is far from the requirments of the current development of the laboratory medicine industry in China. In order to effectively alleviate the current situation of the shortage of practical high-quality laboratory medicine talents,it is urgent to reform the teaching of laboratory medicine. The author of this paper puts forward the "Four-in-one" personnel training mode which is suitable for the development of laboratory medicine in China. It is proposed to explore a new teaching mode on the basis of the existing one,giving full play to the students' initiative and innovation,and providing new ideas for training high-quality laboratory medicine professionals.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

To explore the value of general information and rapid laboratory tests obtained from the emergency department in the early diagnosis and prevention of young patients with acute myocardial infarction and acute myocarditis, in order to prevent the disease from progressing to a critical stage. This study employs a retrospective observational study, compiling clinical data from young patients diagnosed with acute myocardial infarction or acute myocarditis who were admitted to the Department of Cardiology or Emergency Department of the Second Affiliated Hospital of Soochow University from January 2015 to September 2024. Demographic information and laboratory test results from both the outpatient and emergency departments were retrieved. The acute myocardial infarction group comprised 267 patients (257 males, 10 females) aged 23-44 ys, while the acute myocarditis group included 134 patients (93 males, 41 females) aged 18-44 ys. A comparative analysis of the clinical data between the two groups was conducted, encompassing variables such as age, gender, comorbidities, high-risk factors, emergency blood routine tests, high-sensitivity C-reactive protein levels, coagulation profiles, renal function tests, NT-proBNP levels, myocardial injury markers, electrocardiogram readings, blood pressure, and heart rate. The results showed that:Compared with the young myocarditis group, the myocardial infarction group was older (ys)[38(35, 42) vs 30(25, 37), U=7 893, P<0.001], more male [257(96.3%) vs 93(69.4%), χ2=57.95, P<0.001], more smoking [211(79.0%) vs 38(28.4%), χ2=97.32, P<0.001], drinking history [125(46.8%) vs 22(16.4%), χ2=35.51, P<0.001], family history of coronary heart disease [45(16.9%) vs 3(2.2%), χ2=18.09, P<0.001], hypertension [100(37.5%) vs 12(9.0%), χ2=36, P<0.001] and diabetes [42(15.7%) vs 4(3.0%), χ2=14.27, P<0.001]. Systolic blood pressure (mmHg)[126(114, 144) vs 119(101, 126), U=11 389.50, P<0.001], diastolic blood pressure (mmHg)[80(70, 93) vs 72(62, 81), U=12 220.50, P<0.001], total white blood cell count (10 9/L)[11.3(9.2, 14.1) vs 8.5(6.6, 11.2), U=10 825.50, P<0.001], hemoglobin (g/L)[157(147, 166) vs 143(129, 154), U=9 404.50, P<0.001], platelet count (10 9/L)[244(206, 297) vs 207(173, 253), U=11 680, P<0.001], uric acid (μmol/L)[380(315, 446) vs 347(265, 412), U=14 805.50, P=0.005], ST segment elevation [204(76.4%) vs 57(42.5%), χ2=73.03, P<0.001] and Q wave formation [76(28.5%) vs 17(12.7%), χ2=12.47, P<0.001] in ECG were higher than those in myocarditis group. The duration of onset (hs) [6(3, 25) vs 48(24, 73), U=27911, P<0.001], heart rate (beats/min)[82(74, 92) vs 92(78, 103), U=22 347, P<0.001], D-dimer (μg/ml)[0.23(0.17, 0.51) vs 0.61(0.30, 1.38), U=25 806, P<0.001], High-sensitivity troponin T/99th percentile upper reference limit [5(1, 36) vs 16(8, 39), U=22 577, P<0.001], NT-proBNP (pg/ml) [204(64, 644) vs 824(189, 4 043), U=25 134, P<0.001], C-reactive protein (mg/L)[6(3, 9) vs 24(6, 55), U=26 349.50, P<0.001] and body temperature (℃) [36.50(36.30, 36.60) vs 37.35(36.50, 38.50), U=26 961, P<0.001] were significantly lower than those in myocarditis group, the symptoms of chest pain in myocardial infarction group was significantly higher than those in myocarditis group [262(98.1%) vs 83(61.9%), χ2=97.24, P<0.001], and the history of prodromal infection [12(4.5%) vs 112(83.6%), χ2=261.26, P<0.001], syncope [11(4.1%) vs 18(13.4%), χ2=11.53, P<0.001] and shock [6(2.2%) vs 22(16.4%), χ2=27.59, P<0.001] in myocardial infarction group were significantly lower than those in myocarditis group. With acute myocardial infarction as the target outcome, 8 influencing factors selected by LASSO regression, and 5 independent influencing factors were found after multiple Logistic regression, those were age ( OR=1.21, 95% CI: 1.12-1.31; P<0.001), pre-infection ( OR=0.02, 95% CI: 0.01-0.06; P<0.001), body temperature ( OR=0.37, 95% CI: 0.18-0.77; P=0.008), chest pain ( OR=26.75, 95% CI: 5.87-121.81; P<0.001) and white blood cell count ( OR=1.27, 95% CI: 1.12-1.44; P<0.001). Younger age, high body temperature and pre-infection are independent predictors for acute myocarditis, while chest pain and elevated white blood cell count are independent predictors for acute myocardial infarction. The five influencing factors selected by multivariate logistic regression and their combined diagnostic model were subjected to ROC analysis. The AUC reached 0.969, sensitivity reached 0.940 and specificity reached 0.925. Calibration curve and decision curve analysis(DCA) demonstrate that the model possesses excellent clinical application value. In conclusion, age, chest pain, pre-infection, body temperature and white blood cell count were independent factors in distinguishing acute myocardial infarction and acute myocarditis in young people. The clinical differential diagnosis model based on 5 independent factors may has high efficiency and good clinical practicability.